Reading in the data:
lapply(c("plyr","dplyr","ggplot2","cowplot","lubridate",
"tidyverse","data.table","xts","dygraphs",
"nrlmetab","cowplot"), require, character.only=T)
source("./saved_fxns/LM.o2.at.sat.R")
source("./saved_fxns/LM.wind.scale.R")
## MiniDOT sensors - Glenbrook
GBNS1 <- read.csv("./AggRawData/AggregatedDO/GBNS1.csv", header=T)
GBNS2 <- read.csv("./AggRawData/AggregatedDO/GBNS2.csv", header=T)
GBNS3 <- read.csv("./AggRawData/AggregatedDO/GBNS3.csv", header=T)
## MiniDOT sensors - Blackwood
BWNS1 <- read.csv("./AggRawData/AggregatedDO/BWNS1.csv", header=T)
BWNS2 <- read.csv("./AggRawData/AggregatedDO/BWNS2.csv", header=T)
BWNS3 <- read.csv("./AggRawData/AggregatedDO/BWNS3.csv", header=T)
## Weather station
east_ws <- read.csv("./AggRawData/Climate/EastNSclimate.csv")
west_ws <- read.csv("./AggRawData/Climate/WestNSclimate.csv")
Adjusting output files for relevant columns:
## Adjust miniDOT data
miniDOT_adj <- function(x){
df <- x %>% dplyr::select(timestamp, DO, Temp, site) %>%
dplyr::rename(datetime = timestamp, do.obs = DO, wtr = Temp, site = site) %>%
mutate(datetime = as.POSIXct((datetime), format ="%Y-%m-%d %H:%M:%S"))
return(df)
}
# Glenbrook sensors
GS1 <- miniDOT_adj(GBNS1)
GS2 <- miniDOT_adj(GBNS2)
GS3 <- miniDOT_adj(GBNS3)
# Blackwood sensors
BS1 <- miniDOT_adj(BWNS1)
BS2 <- miniDOT_adj(BWNS2)
BS3 <- miniDOT_adj(BWNS3)
Function plotting DO data and looking for unusual peaks:
## GS1 DO corrections
vis_data_DO(GS1)
GS1_adj <- GS1[-which(GS1$datetime >= "2021-10-29 11:30:00" & GS1$datetime <= "2021-10-29 14:00:00"),]
GS1_adj <- GS1[-which(GS1$datetime >= "2022-04-05 00:00:00" & GS1$datetime <= "2022-05-25 00:00:00"),]
vis_data_DO(GS1_adj)
## GS2 DO corrections
vis_data_DO(GS2)
GS2_adj <- GS2[-which(GS2$datetime >= "2021-08-24 00:00:00" & GS2$datetime <= "2021-10-02 00:00:00"),]
GS2_adj <- GS2_adj[-which(GS2_adj$datetime >= "2022-04-05 00:00:00" & GS2_adj$datetime <= "2022-05-25 00:00:00"),]
vis_data_DO(BS2)